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A Unified Framework for Reservoir Computing and Extreme Learning Machines based on a Single Time-delayed Neuron

AuthorsOrtín González, Silvia CSIC ORCID; Soriano, Miguel C. ; Pesquera, Luis CSIC ORCID ; Brunner, Daniel CSIC ORCID; San-Martín, Daniel; Fischer, Ingo CSIC ORCID ; Mirasso, Claudio R. CSIC ORCID ; Gutiérrez, José M. CSIC ORCID
Issue Date2015
PublisherNature Publishing Group
CitationScientific Reports 5: 14945 (2015)
AbstractIn this paper we present a unified framework for extreme learning machines and reservoir computing (echo state networks), which can be physically implemented using a single nonlinear neuron subject to delayed feedback. The reservoir is built within the delay-line, employing a number of virtual neurons. These virtual neurons receive random projections from the input layer containing the information to be processed. One key advantage of this approach is that it can be implemented efficiently in hardware. We show that the reservoir computing implementation, in this case optoelectronic, is also capable to realize extreme learning machines, demonstrating the unified framework for both schemes in software as well as in hardware.
Publisher version (URL)http://dx.doi.org/10.1038/srep14945
Identifiersissn: 2045-2322
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